Frontier

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already fully cover the safety profile with readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the specific contents of the response but discloses no additional behavioral traits such as authentication requirements, rate limits, or pagination, although for a simple getter this is minimally adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, tightly written sentence that leads with the action and directly enumerates the response contents. Every word earns its place, with no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, comprehensive annotations, and an existing output schema, the description sufficiently scopes the tool. It could benefit from a brief comparison to sibling tools, but it is otherwise complete for a capabilities getter.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and the schema description coverage is 100%, so there are no parameter details for the description to add. Per the rubric, the no-parameter baseline of 4 applies here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Get') and names the exact resources retrieved: public endpoints, stable aliases, and Chat Completions constraints. This clearly distinguishes it from sibling tools like list_models and get_usage by focusing on capability details rather than model listings or usage statistics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for discovering capabilities before making API requests, but it provides no explicit guidance on when to use it versus the sibling tools. There are no stated alternatives or exclusions, so the agent must infer the appropriate context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare this as read-only, idempotent, and non-destructive. The description adds context about the key and request scoping, which is useful, but does not disclose additional behaviors such as rate limits or response format. This aligns with the expected level given annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that conveys the essential purpose and scope without any wasted words. It is appropriately concise for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's minimal complexity, empty parameter schema, existing output schema, and rich annotations, the description fully covers what the agent needs to know. There is no missing contextual information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema is trivially complete. The description does not need to explain parameter semantics, earning the baseline score for parameter-free tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'Read' and identifies the exact resource (usage for the Frontier bearer API key) plus the scope ('on this MCP HTTP request'). This clearly distinguishes it from sibling tools like get_capabilities and list_models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives. It relies on the implied meaning of 'Read usage' but offers no exclusions or comparison to sibling tools, leaving the AI agent to infer applicability.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint, providing the safety and idempotency profile. The description adds minimal context by specifying 'current stable public', which conveys that the list is a snapshot of public stable aliases and may change in the future. However, it does not describe return format or pagination, though the output schema likely covers that.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-formed sentence that immediately states the verb and object. It is front-loaded with the action and resource, contains no filler, and is appropriately sized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with an output schema and strong annotations, the description is fully adequate. It states exactly what is returned ('current stable public model aliases') and does not need to explain return values because an output schema exists. The simplicity of the tool means no additional behavioral context is required.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema is complete (100% coverage vacuously). The description need not explain parameters. The baseline for zero params is 4, and the description does not detract from that.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'List Frontier's current stable public model aliases' uses a specific verb ('list') and resource ('model aliases'), and adds scope ('current stable public'), making the tool's purpose unambiguous. It clearly distinguishes from sibling tools like get_capabilities and get_usage, which invoke different resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention get_capabilities or get_usage, nor does it state any conditions or exclusions. The only implied context is that the tool lists model aliases, but that is inferred from the purpose, not stated as usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.